the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Feb 2025
Brief Communication: Daily, gap-free snow cover information based on a combination of NPP VIIRS and MODIS data
Abstract. Combining Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) snow cover data and applying cloud- and data gap interpolation steps can be utilized to generate daily, gap-free snow cover information. Provided by the German Aerospace Center (DLR) under the name “Global SnowPack”, this product has undergone several improvements, comprising the inclusion of NPP VIIRS, a new threshold for the Normalized Difference Snow index (NDSI), and considerable validation. The Global SnowPack offers unique opportunities for analyzing time series of snow cover data since September 2000 and is freely available for visualization and download from DLRs GeoService.
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Status: open (until 24 Apr 2025)
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RC1: 'Comment on egusphere-2025-382', Anonymous Referee #1, 24 Mar 2025
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General comments
This short paper presents an upgrade of GSP, a daily global gap-free snow cover extent dataset derived from NASA MODIS and VIIRS snow products. The main difference with the previous GSP dataset (Dietz et al. 2015) lies in the integration of VNP10A1 products. As a result, the new GSP dataset has a finer resolution of 371 m instead of 500 m. The manuscript also includes an evaluation of the GSP dataset using Landsat images.
The GSP dataset offers an interesting compromise between coverage (global), spatial resolution (371 m), latency (3 days) and ease of access (very intuitive interface). The added value of VIIRS data is considerable and well highlighted in Figure 3. The convenient data formatting and encoding makes it easy to use for climate studies.
However, the article has several shortcomings.
- the introduction does not explain the stated what’s the added value of your GSP dataset with respect to other similar gap-free products (e.g. Modis gap filled M[O-Y]D10A1F) or IMS (https://doi.org/10.7265/N52R3PMC). A review of other snow cover extent products is clearly behind the scope of the paper, but there could be the room for briefly highlighting why one should prefer this dataset.
- The evaluation of the GSP was performed using in situ data and Landsat products. Regarding Landsat the only result is one sentence “The comparison with Landsat resulted in an F1 score of 0.94”. The discussion of the results is very limited. In the conclusion the authors wrote that the “uncertainties can easily be estimated and included in an uncertainty layer”, however there is no further indication on how to actually estimate this uncertainty and the product does not provide such layer. The lack of information regarding Landsat products and the minimalist description of the results could prevent potential users from using this dataset.
Minor comments
- In the absence of a user manual, more details about the dataset would be useful for users: is it operational ? What’s the latency ? In which grid is it distributed ?
- L28: An illustration (a sample) of the dataset would be a nice addition.
- L32: « Landsat data » Which Landsat ?
- L33: « A total of 381 Landsat scenes » : the geographical and temporal distribution of these tiles is briefly given later (“distributed worldwide and covering all ecozones and seasons”). It would be more straightforward to give this information in paragraph 2 (and not 2.1). Also, additional information about these tiles could be useful: are mountain regions and high latitudes covered? In which years were these images acquired? .
- L33-35: The sentence is confusing. A reader cannot understand how the reference data were prepared. In (Koeheler et al. 2022) the NDSI is defined between a blue and a NIR band, in SnowPex exercise there are few different methods used for snow cover detection. Can you clarify?
- L36: GMTED2010 is a multi resolution dataset. Which spatial resolution was used?
- L39-41: it is unnecessary to repeat here the motivation of this work especially in a “Brief communication”.
- L43: The daily combination step could potentially benefit from satellite viewing angle information. Plenty of evaluations show how MODIS/VIIRS information is more reliable closer to nadir [1], [2], [3].
- L55: What is the 3000 in the parenthesis next to MODIS products ?
- L66-77: should be in the Data & Method section.
- L70 (Figure 2 legend) : MOD10 identifier is a bit confusing here. MOD10 refers to MODIS/Terra products (e.g. MOD10A2). Please refer to the products ID (MOD10A1,MYD10A1,VNP10A1).
- L70 (Figure 2): maybe a logarithmic scale for the snow depth threshold would be more appropriate.
- L80: « This accuracy….Figure 2b). » Can you elaborate? Accuracy reaches a maximum during summer months and decreases in the winter, meaning that accuracy is lower when the snow cover is greater at global scales. Any explanation?
- L81 Again inconsistent notation MOD10/VIIRS.
- L84 (Figure 3) Can you specify for which year or years was this plot computed?
References
[1] K. R. Arsenault, P. R. Houser, et G. J. M. De Lannoy, « Evaluation of the MODIS snow cover fraction product », Hydrological Processes, vol. 28, no 3, p. 980‑998, 2014, doi: 10.1002/hyp.9636.
[2] K. Rittger, K. J. Bormann, E. H. Bair, J. Dozier, et T. H. Painter, « Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI », Frontiers in Remote Sensing, vol. 2, 2021,
[3] K. Rittger, K. J. Bormann, E. H. Bair, J. Dozier, et T. H. Painter, « Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI », Frontiers in Remote Sensing, vol. 2, 2021,
Citation: https://doi.org/10.5194/egusphere-2025-382-RC1
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